The proposed segmentation strategy includes both synthesis of missing contrasts with a generator and a segmentor (Figure REF ). In the generation stage, we adapted popular pix2pix [1]} conditional GAN method for synthesising all synthesized contrasts from real (true) contrasts. Any other GAN method can be used too. Briefly, pix2pix uses a conditional generative adversarial network to learn mapping from a source domain \(x\)  and random noise \(z\)  to an target domain \(y\) . The network is made up of two blocks, the generator \({G}\) , and the discriminator \({D}\) . The generator transforms the source domain (\(x\) ) with random noise (\(z\) ) to get the target domain (\(y\) ) while discriminator learns how much target domain is similar to source domain. As shown in Figure  REF , then we use all real MR contrasts (R MRI) for synthesizing (generating) other contrasts (F MRI) using Equation REF  and Equation REF  where \(x\)  is source contrast, \(y\)  is target contrast, \(z\)  is random noise, \(\lambda \)  is hyperparameter for adjusting blurriness, and \(\mathcal {L}_{L 1}\)  mean square error:
\(\begin{aligned}\mathcal {L}_{c G A N}(G, D)=& \mathbb {E}_{x, y}[\log D(x, y)]+\\& \mathbb {E}_{x, z}[\log (1-D(x, G(x, z))].\end{aligned}\) 
\(G^{*}=\arg \min _{G} \max _{D} \mathcal {L}_{c G A N}(G, D)+\lambda \mathcal {L}_{L 1}(G).\) 
